Cluster Computing

, Volume 22, Supplement 6, pp 15387–15408 | Cite as

A unified algorithm to automatic semantic composition using multilevel workflow orchestration

  • U. ArulEmail author
  • S. Prakash


As a result of state-of-the-art development in service oriented architecture, we need a composition framework and comprehensive algorithm to discover and compose the web services from different environments. In this paper, we present a unified semantic-oriented framework with corresponding algorithm for automatic web service composition that integrates the comprehensive process of modified multistage composition and rigor of web semantics. Our proposed unified algorithm introduces the novel features through modified five stage composition such as transformation of non-functional properties of user requirements to all stages, optimization and semantic validation of abstract workflows using workflow automata, annotating WSDL files with additional ontologies using ontology based service repository, adopting dynamic change of user requirements for discovering candidate services, and selecting most optimal services for concrete composition using non-functional properties are effectively represented. Feasible composition solution obtained for user complex requirements through semantic web service discovery mechanism for discovering and selecting the most suitable service candidates. Furthermore, our unified algorithm can provide a composition solution through wider acceptance of semantics-oriented documents such as web ontology language for services and web service modeling ontology. We evaluate the proposed unified algorithm for automatic generation of composition using our motivating scenario, namely, home loan approval inference process. We also evaluate algorithm for automated and dynamic composition on service repositories of various sizes in increasing the levels of nesting and present the performance results.


SOA Automatic semantic composition Workflow orchestration Non-functional properties Levels of nesting SWSD 


  1. 1.
    Rao, J., Su, X.: A survey of automated web service composition methods. Semant. Web Serv. Web Process Compos. 3387, 43–54 (2005)CrossRefGoogle Scholar
  2. 2.
    Kona, S., Bansal, A., Blake, M.B., Gupta, G.: Generalized semantics-based service composition. In: IEEE International Conference on Web Services (ICWS), pp. 219–227 (2008)Google Scholar
  3. 3.
    Srivastava, B., Koehler, J.: Web service composition: current solutions and open problems. In: Proceedings of Workshop Planning for Web Services (2003)Google Scholar
  4. 4.
    Kona, S., Bansal, A., Simon, L., Mallya, A., Gupta, G.: USDL: aservice-semantics description language for automatic service discoveryand composition. Int. J. Web Serv. Res. 6(1), 20–48 (2009)CrossRefGoogle Scholar
  5. 5.
    Milanovic, N., Malek, M.: Current solutions for web service composition. IEEE Internet Comput. 8(6), 51–59 (2004)CrossRefGoogle Scholar
  6. 6.
  7. 7.
    Casati, F. et al.: Adaptive and Dynamic Service Composition in eFlow. In: Proceeding of 12th International Conference on Advanced Information Systems Engineering(CAiSE’00) (2000)Google Scholar
  8. 8.
    Pistore, M., Marconi, A., Bertoli, P., Traverso, P.: Automated composition of web services by planning at the knowledge level. In: Proceeding of 19th International Joint Conference on Artificial Intelligence (IJCAI’05) (2005)Google Scholar
  9. 9.
    Rao, J., Dimitrov, D., Hofmann, P., Sadeh, N.: A mixed initiative approach to semantic web service discovery and composition: SAP’s guided procedures framework. In: International Conference on Web Services. ICWS’06, 2006, pp. 401–410 (2006)Google Scholar
  10. 10.
    McIlraith, S., Son, T.C.: Adapting golog for composition of semantic web services. KR 2, 482–493 (2002)Google Scholar
  11. 11.
    Pistore, M., Roberti, P., Traverso, P.: Process-level composition of executable web services: on-the-fly versus once-for-all composition. In: Gomez-Perez, A., Euzenat, J. (eds.) The Semantic Web: Research and Applications, pp. 62–77. Springer, Berlin (2005)CrossRefGoogle Scholar
  12. 12.
    Zhan, R., Arpinar, B., Aleman-Meza, B.: Automatic compositionof semantic web services. In: Proceeding of International Conference on Web Services(ICWS’03) (2003)Google Scholar
  13. 13.
    Sirin, E., Parsia, B., Wu, D., Hendler, J., Nau, D.: HTN planning for web service composition using Shop2. J. Web Semant. 1(4), 377–396 (2004)CrossRefGoogle Scholar
  14. 14.
    Feng, Y., Ngan, L.D., Kanagasabai, R.: Dynamic service composition with service-dependent QoS attributes. In: 2013 IEEE 20th international conference on web services (ICWS), pp. 10–17 (2013)Google Scholar
  15. 15.
    McIlraith, S., Son, T.: Adapting golog for composition of semantic web services. In: Proceeding of Eighth International Conference on Knowledge Representation and Reasoning, pp. 482–493 (2002)Google Scholar
  16. 16.
    Ponnekanti, S.R., Fox, A.: SWORD: a developer toolkit for web service composition. In: Proceeding of 11th International WWW Conference (WWW’02), pp. 83–107 (2002)Google Scholar
  17. 17.
    Klusch, M., Gerber, A., Schmidt, M.: Semantic web service composition planning with OWLS-XPlan. In: Proceedings of AAAI Fall Symposium onSemantic Web and Agents (2005)Google Scholar
  18. 18.
    Dong, W., Jiao, L.; QoS-awareWeb service composition based on SLA. In: Fourth International Conference on Natural Computation (ICNC), vol. 5, pp. 247–251 (2008)Google Scholar
  19. 19.
    Yan, J., Kowalczyk, R., Lin, J., Chhetri, M.B., Goh, S.K., Zhang, J.: Autonomous service level agreement negotiation for service composition provision. Future GenerComputSyst 23(6), 748–759 (2007)Google Scholar
  20. 20.
    Wada, H., Champrasert, P., Suzuki, J., Oba, K.: Multiobjective optimization of sla-aware service composition. In: IEEE Congress on Services-Part I, 2008, pp. 368–375. IEEE (2008)Google Scholar
  21. 21.
    Claro, D.B., Albers, P., Hao, J.K.: Selecting web services for optimal composition. In: ICWS International Workshop on Semantic and Dynamic Web Processes, Orlando-USA (2005)Google Scholar
  22. 22.
    Ramanathan, R., Latha, B.: Towards optimal resource provisioning for Hadoop-MapReduce jobs using scale-out strategy and its performance analysis in private cloud environment. Clust. Comput. (2018). CrossRefGoogle Scholar
  23. 23.
  24. 24.
  25. 25.
    Bellahsene, Z., Bonifati, A., Rahm, E.: Schema Matching and Mapping. Springer, New York (2011)CrossRefGoogle Scholar
  26. 26.
    Algergawy, A., Nayak, R., Siegmund, N., K¨oppen, V., Saake, G.: Combining schema and level-based matching for web service discovery. In: ICWE (2010), pp 114–128 (2010)Google Scholar
  27. 27.
    Vijayakumar, K., Arun, C.: Automated risk identification using NLP in cloud based development environments. J. Ambient Intell. Hum. Comput. (2017). CrossRefGoogle Scholar
  28. 28.
    Vijayakumar, K., Arun, C.: Continuous security assessment of cloud based applications using distributed hashing algorithm in SDLC. Clust. Comput. (2017). CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringDhanalakshmi College of EngineeringChennaiIndia
  2. 2.Department of Electronics and Communication EngineeringJerusalem College of EngineeringChennaiIndia

Personalised recommendations